from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 33.0 | 1.873187 |
| daal4py_KNeighborsClassifier | 0.0 | 6.0 | 26.959547 |
| KNeighborsClassifier_kd_tree | 0.0 | 2.0 | 38.264852 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 0.0 | 31.523763 |
| KMeans_tall | 0.0 | 0.0 | 26.326621 |
| daal4py_KMeans_tall | 0.0 | 0.0 | 10.569867 |
| KMeans_short | 0.0 | 0.0 | 3.898321 |
| daal4py_KMeans_short | 0.0 | 0.0 | 2.114823 |
| LogisticRegression | 0.0 | 0.0 | 25.416315 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 5.911150 |
| Ridge | 0.0 | 0.0 | 11.484007 |
| daal4py_Ridge | 0.0 | 0.0 | 2.494304 |
| HistGradientBoostingClassifier | 0.0 | 5.0 | 14.648287 |
| lightgbm | 0.0 | 5.0 | 10.404538 |
| xgboost | 0.0 | 5.0 | 33.241095 |
| catboost | 0.0 | 5.0 | 23.083128 |
| total | 1.0 | 5.0 | 28.323837 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.171 | 0.000 | 4.690 | 0.000 | 1 | 100 | NaN | NaN | 0.553 | 0.000 | 0.308 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 25.003 | 0.955 | 0.000 | 0.025 | 1 | 100 | 0.940 | 0.731 | 4.492 | 0.142 | 5.566 | 0.276 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.232 | 0.009 | 0.000 | 0.232 | 1 | 100 | 1.000 | 0.000 | 0.106 | 0.004 | 2.191 | 0.110 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.160 | 0.000 | 4.995 | 0.000 | 1 | 5 | NaN | NaN | 0.534 | 0.000 | 0.300 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 23.830 | 0.726 | 0.000 | 0.024 | 1 | 5 | 0.807 | 0.941 | 4.592 | 0.110 | 5.190 | 0.201 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.226 | 0.011 | 0.000 | 0.226 | 1 | 5 | 1.000 | 1.000 | 0.111 | 0.006 | 2.029 | 0.150 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.131 | 0.000 | 6.123 | 0.000 | -1 | 1 | NaN | NaN | 0.572 | 0.000 | 0.229 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 30.582 | 0.000 | 0.000 | 0.031 | -1 | 1 | 0.728 | 0.941 | 4.795 | 0.277 | 6.378 | 0.369 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.198 | 0.024 | 0.000 | 0.198 | -1 | 1 | 0.000 | 1.000 | 0.118 | 0.020 | 1.673 | 0.347 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.141 | 0.000 | 5.664 | 0.000 | -1 | 5 | NaN | NaN | 0.580 | 0.000 | 0.244 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 39.612 | 0.000 | 0.000 | 0.040 | -1 | 5 | 0.807 | 0.816 | 4.565 | 0.070 | 8.677 | 0.133 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.191 | 0.012 | 0.000 | 0.191 | -1 | 5 | 1.000 | 0.000 | 0.108 | 0.005 | 1.781 | 0.137 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.131 | 0.000 | 6.089 | 0.000 | -1 | 100 | NaN | NaN | 0.582 | 0.000 | 0.226 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 38.748 | 0.000 | 0.000 | 0.039 | -1 | 100 | 0.940 | 0.816 | 4.587 | 0.041 | 8.447 | 0.075 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.203 | 0.021 | 0.000 | 0.203 | -1 | 100 | 1.000 | 0.000 | 0.107 | 0.004 | 1.894 | 0.210 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.129 | 0.000 | 6.219 | 0.000 | 1 | 1 | NaN | NaN | 0.570 | 0.000 | 0.226 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 17.599 | 0.200 | 0.000 | 0.018 | 1 | 1 | 0.728 | 0.731 | 4.586 | 0.028 | 3.837 | 0.049 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.216 | 0.008 | 0.000 | 0.216 | 1 | 1 | 0.000 | 0.000 | 0.116 | 0.014 | 1.867 | 0.239 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.060 | 0.000 | 0.265 | 0.000 | 1 | 100 | NaN | NaN | 0.112 | 0.000 | 0.539 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 20.816 | 0.787 | 0.000 | 0.021 | 1 | 100 | 0.980 | 0.971 | 1.002 | 0.029 | 20.768 | 0.986 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.002 | 0.000 | 0.020 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 4.343 | 0.510 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.057 | 0.000 | 0.281 | 0.000 | 1 | 5 | NaN | NaN | 0.108 | 0.000 | 0.525 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 20.591 | 0.550 | 0.000 | 0.021 | 1 | 5 | 0.975 | 0.978 | 1.166 | 0.156 | 17.653 | 2.404 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.021 | 0.002 | 0.000 | 0.021 | 1 | 5 | 1.000 | 1.000 | 0.006 | 0.001 | 3.817 | 0.561 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.057 | 0.000 | 0.279 | 0.000 | -1 | 1 | NaN | NaN | 0.131 | 0.000 | 0.438 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 25.066 | 0.452 | 0.000 | 0.025 | -1 | 1 | 0.968 | 0.978 | 1.072 | 0.028 | 23.372 | 0.733 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.018 | 0.002 | 0.000 | 0.018 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.001 | 3.580 | 0.580 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.310 | 0.000 | -1 | 5 | NaN | NaN | 0.104 | 0.000 | 0.497 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 33.432 | 0.000 | 0.000 | 0.033 | -1 | 5 | 0.975 | 0.977 | 0.973 | 0.018 | 34.368 | 0.636 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.028 | 0.003 | 0.000 | 0.028 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 5.370 | 0.763 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.053 | 0.000 | 0.302 | 0.000 | -1 | 100 | NaN | NaN | 0.094 | 0.000 | 0.562 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 32.685 | 0.000 | 0.000 | 0.033 | -1 | 100 | 0.980 | 0.977 | 0.992 | 0.033 | 32.957 | 1.099 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.029 | 0.003 | 0.000 | 0.029 | -1 | 100 | 1.000 | 1.000 | 0.004 | 0.000 | 6.604 | 0.998 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.048 | 0.000 | 0.332 | 0.000 | 1 | 1 | NaN | NaN | 0.089 | 0.000 | 0.544 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 11.768 | 0.182 | 0.000 | 0.012 | 1 | 1 | 0.968 | 0.971 | 1.004 | 0.026 | 11.722 | 0.358 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.011 | 0.001 | 0.000 | 0.011 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.001 | 2.338 | 0.385 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.778 | 0.000 | 0.029 | 0.000 | 1 | 100 | NaN | NaN | 0.802 | 0.000 | 3.464 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.815 | 0.217 | 0.000 | 0.005 | 1 | 100 | 0.974 | 0.978 | 0.215 | 0.007 | 22.411 | 1.262 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.006 | 0.002 | 0.000 | 0.006 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 14.678 | 5.653 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.522 | 0.000 | 0.032 | 0.000 | -1 | 5 | NaN | NaN | 0.776 | 0.000 | 3.248 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.789 | 0.031 | 0.000 | 0.001 | -1 | 5 | 0.973 | 0.974 | 0.654 | 0.015 | 1.207 | 0.054 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 4.619 | 2.283 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.747 | 0.000 | 0.029 | 0.000 | -1 | 1 | NaN | NaN | 0.745 | 0.000 | 3.688 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.452 | 0.014 | 0.000 | 0.000 | -1 | 1 | 0.960 | 0.978 | 0.223 | 0.024 | 2.028 | 0.229 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 10.099 | 5.431 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.007 | 0.000 | 0.027 | 0.000 | 1 | 1 | NaN | NaN | 0.731 | 0.000 | 4.113 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.794 | 0.013 | 0.000 | 0.001 | 1 | 1 | 0.960 | 0.968 | 0.123 | 0.009 | 6.443 | 0.503 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 4.424 | 3.392 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.932 | 0.000 | 0.027 | 0.000 | -1 | 100 | NaN | NaN | 0.717 | 0.000 | 4.090 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.634 | 0.103 | 0.000 | 0.003 | -1 | 100 | 0.974 | 0.968 | 0.116 | 0.006 | 22.677 | 1.511 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.010 | 0.002 | 0.000 | 0.010 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 35.934 | 19.609 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.667 | 0.000 | 0.030 | 0.000 | 1 | 5 | NaN | NaN | 0.684 | 0.000 | 3.901 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.419 | 0.054 | 0.000 | 0.001 | 1 | 5 | 0.973 | 0.974 | 0.631 | 0.017 | 2.250 | 0.104 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 2.097 | 0.911 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.822 | 0.000 | 0.019 | 0.000 | 1 | 100 | NaN | NaN | 0.497 | 0.000 | 1.655 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.065 | 0.003 | 0.000 | 0.000 | 1 | 100 | 0.986 | 0.976 | 0.001 | 0.000 | 45.380 | 14.699 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 4.898 | 2.067 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.834 | 0.000 | 0.019 | 0.000 | -1 | 5 | NaN | NaN | 0.502 | 0.000 | 1.662 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.041 | 0.004 | 0.000 | 0.000 | -1 | 5 | 0.983 | 0.980 | 0.008 | 0.001 | 5.188 | 0.707 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 13.435 | 8.370 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.766 | 0.000 | 0.021 | 0.000 | -1 | 1 | NaN | NaN | 0.550 | 0.000 | 1.393 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.041 | 0.002 | 0.000 | 0.000 | -1 | 1 | 0.975 | 0.976 | 0.001 | 0.000 | 31.494 | 8.521 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 16.271 | 9.195 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.772 | 0.000 | 0.021 | 0.000 | 1 | 1 | NaN | NaN | 0.490 | 0.000 | 1.575 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.039 | 0.002 | 0.000 | 0.000 | 1 | 1 | 0.975 | 0.969 | 0.001 | 0.000 | 43.763 | 11.742 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.018 | 3.624 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.793 | 0.000 | 0.020 | 0.000 | -1 | 100 | NaN | NaN | 0.526 | 0.000 | 1.508 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.052 | 0.004 | 0.000 | 0.000 | -1 | 100 | 0.986 | 0.969 | 0.001 | 0.000 | 58.628 | 19.327 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 15.541 | 9.048 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.779 | 0.000 | 0.021 | 0.000 | 1 | 5 | NaN | NaN | 0.505 | 0.000 | 1.541 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.042 | 0.002 | 0.000 | 0.000 | 1 | 5 | 0.983 | 0.980 | 0.008 | 0.001 | 5.535 | 0.749 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 5.404 | 3.434 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.647 | 0.0 | 0.742 | 0.000 | random | NaN | 30 | NaN | 0.353 | 0.0 | 1.833 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.0 | 0.308 | 0.000 | random | 0.001 | 30 | 0.002 | 0.000 | 0.0 | 6.389 | 2.846 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.0 | 0.000 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 7.898 | 4.354 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.696 | 0.0 | 0.690 | 0.000 | k-means++ | NaN | 30 | NaN | 0.296 | 0.0 | 2.351 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.0 | 0.268 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 7.753 | 3.739 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.0 | 0.000 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.219 | 5.127 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 7.950 | 0.0 | 3.019 | 0.000 | random | NaN | 30 | NaN | 4.106 | 0.0 | 1.936 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 13.068 | 0.000 | random | 0.002 | 30 | 0.001 | 0.000 | 0.0 | 5.308 | 2.394 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.0 | 0.015 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 7.986 | 4.213 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 7.926 | 0.0 | 3.028 | 0.000 | k-means++ | NaN | 30 | NaN | 3.945 | 0.0 | 2.009 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 13.006 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.526 | 2.714 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.0 | 0.014 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.737 | 4.730 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 20 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.109 | 0.0 | 0.029 | 0.000 | random | NaN | 20 | NaN | 0.059 | 0.000 | 1.845 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.135 | 0.000 | random | 0.000 | 20 | -0.000 | 0.001 | 0.000 | 3.291 | 0.825 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.0 | 0.000 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.000 | 9.578 | 5.303 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.383 | 0.0 | 0.008 | 0.000 | k-means++ | NaN | 20 | NaN | 0.143 | 0.000 | 2.680 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.147 | 0.000 | k-means++ | 0.003 | 20 | -0.001 | 0.001 | 0.000 | 2.713 | 0.718 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.0 | 0.000 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.000 | 7.482 | 4.036 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.347 | 0.0 | 0.461 | 0.000 | random | NaN | 20 | NaN | 0.296 | 0.000 | 1.171 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.0 | 5.177 | 0.000 | random | 0.232 | 20 | 0.247 | 0.002 | 0.001 | 1.430 | 0.901 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.0 | 0.009 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.000 | 8.002 | 3.488 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 1.233 | 0.0 | 0.130 | 0.000 | k-means++ | NaN | 20 | NaN | 0.715 | 0.000 | 1.725 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.004 | 0.0 | 4.357 | 0.000 | k-means++ | 0.264 | 20 | 0.359 | 0.002 | 0.000 | 2.277 | 0.404 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.0 | 0.009 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.000 | 6.723 | 2.962 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 15.804 | 0.0 | [-0.07465681] | 0.000 | NaN | NaN | NaN | NaN | NaN | 3.018 | 0.0 | 5.236 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [41.99145527] | 0.000 | NaN | NaN | NaN | NaN | 0.534 | 0.000 | 0.0 | 0.785 | 0.373 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.12454269] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.000 | 0.0 | 0.374 | 0.390 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [28] | 1.446 | 0.0 | [-1.42091244] | 0.001 | NaN | NaN | NaN | NaN | NaN | 1.277 | 0.0 | 1.133 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [28] | 0.003 | 0.0 | [84.08914711] | 0.000 | NaN | NaN | NaN | NaN | 0.330 | 0.004 | 0.0 | 0.637 | 0.086 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [28] | 0.000 | 0.0 | [15.87076662] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.141 | 0.085 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.320 | 0.000 | 0.250 | 0.0 | NaN | NaN | NaN | 0.311 | 0.000 | 1.030 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.012 | 0.001 | 6.731 | 0.0 | NaN | NaN | 0.118 | 0.019 | 0.002 | 0.624 | 0.073 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.000 | 0.589 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.707 | 0.564 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.593 | 0.000 | 0.502 | 0.0 | NaN | NaN | NaN | 0.422 | 0.000 | 3.779 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.000 | 2.328 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.000 | 1.194 | 1.263 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.000 | 0.008 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.679 | 0.521 | See | See |
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
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"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
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"version": null,
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}
],
"cpu_count": 2
}